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Incremental 2D self-labelling for effective 3D medical volume segmentation with minimal annotations.
Matthew Anderson1, Maged Habib2,3, David H Steel2,3
1School of Computing, Newcastle University, 1, Urban Sciences Building, Science Square, 1 Science Square, Newcastle Upon Tyne, NE4 5TG, UK.
BMC Medical Imaging
|November 7, 2025
Summary
This study introduces a 2D self-labelling framework to improve 3D medical image segmentation with minimal annotations. The method significantly enhances segmentation accuracy and 3D continuity, reducing annotation costs.
Area of Science:
- Medical Imaging
- Deep Learning
- Computational Anatomy
Background:
- Deep learning models excel in medical image segmentation but require extensive annotated data.
- Acquiring fully annotated datasets is costly and labor-intensive, hindering advancements.
- This study addresses the challenge of training models with limited annotations.
Purpose of the Study:
- To explore the feasibility of training 2D models under severe annotation constraints.
- To optimize segmentation performance while minimizing annotation costs.
- To develop a computationally efficient method for 3D medical volume segmentation.
Main Methods:
- An incremental 2D self-labelling framework was developed for 3D medical volume segmentation.
- A 2D U-Net was trained on a single annotated slice per volume.
- The model iteratively generated and refined pseudo-labels for adjacent slices, progressively fine-tuning itself.
Main Results:
- The self-labelling approach significantly improved segmentation performance on brain MRI and liver CECT datasets.
- Dice Similarity Coefficient and Intersection over Union increased by up to 15.95% and 26.75%, respectively.
- 3D continuity was enhanced, reducing the 95th percentile Hausdorff Distance from 69.88 mm to 36.46 mm.
Conclusions:
- The proposed framework effectively leverages 2D models with self-labelling for robust 3D segmentation.
- This method achieves strong performance and coherence even with extremely sparse annotations.
- The approach offers a viable solution to reduce the annotation burden in medical imaging.
